Investigation generation in an observation and surveillance system

ABSTRACT

The present disclosure is directed to systems and methods for generating investigations of user behavior. In an example embodiment, the system includes a video camera configured to capture video of user activity, a video analytic module to perform real-time video processing of the captured video to generate non-video data from video, and a computer configured to receive the video and the non-video data from the video camera. The computer includes a video analytics module configured to analyze one of video and non-video data to identify occurrences of particular user behavior, and an investigation generation module configured to generate an investigation containing at least one video sequence of the particular user behavior. In some embodiments, the investigation is generated in near real time. The particular user behavior may be defined as an action, an inaction, a movement, a plurality of event occurrences, a temporal event and/or an externally-generated event.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/458,846, filed on Jul. 1, 2019, now U.S. Pat. No. 10,846,971, whichis a continuation application of U.S. patent application Ser. No.15/729,135, filed on Oct. 10, 2017, now U.S. Pat. No. 10,347,070, whichis a continuation application of U.S. patent application Ser. No.14/213,548, filed on Mar. 14, 2014, now U.S. Pat. No. 9,786,113, whichclaims the benefit of and priority to U.S. Provisional Application Ser.No. 61/798,740, filed on Mar. 15, 2013. The disclosures of all of theforegoing applications are incorporated in their entirety by referenceherein.

BACKGROUND 1. Technical Field

The following relates to video observation, surveillance andverification systems and methods of use. The specific application maywork in conjunction with a system providing external data such as, forexample, a point of sale (POS) transaction system that will be describedherein, however, information may be provided from any external datasystem related to transactions in health care facilities, restaurants,and the like.

2. Background of Related Art

Companies are continually trying to identify specific user behavior inorder to improve the throughput and efficiency of the company. Forexample, by understanding user behavior in the context of the retailindustry, companies can both improve product sales and reduce productshrinkage. Focusing on the latter, employee theft is one of the largestcomponents of retail inventory shrink. Therefore, companies are tryingto understand user behavior in order to reduce and ultimately eliminateinventory shrinkage.

Companies have utilized various means to prevent employee shrinkage.Passive electronic devices attached to theft-prone items in retailstores are used to trigger alarms, although customers and/or employeesmay deactivate these devices before an item leaves the store. Someretailers conduct bag and/or cart inspections for both customers andemployees while other retailers have implemented loss prevention systemsthat incorporate video monitoring of POS transactions to identifytransactions that may have been conducted in violation of implementedprocedures. Most procedures and technologies focus on identifyingindividual occurrences instead of understanding the underlying userbehaviors that occur during these events. As such, companies are unableto address the underlying condition that allows individuals to committheft.

SUMMARY

Embodiments described herein may be framed in the context of retailshrinkage, although the systems and methods described herein can beapplied to other retail or service industries such as health carefacilities, restaurants, and the like.

In one aspect, the present disclosure is directed to a system togenerate real-time investigations of user behavior. In an exampleembodiment, the system includes a video camera configured to capturevideo of user activity, a video analytic module to perform real-timevideo processing of the captured video to generate non-video data fromvideo, and a computer configured to receive the video and the non-videodata from the video camera. The computer includes a video analyticsmodule configured to analyze one of video and non-video data to identifyoccurrences of particular user behavior, and an investigation generationmodule configured to generate an investigation containing at least onevideo sequence of the particular user behavior. In some embodiments, theinvestigation is generated in near real time. The particular userbehavior may be defined as an action, an inaction, a movement, aplurality of event occurrences, a temporal event and/or anexternally-generated event.

In some embodiments, the investigation generation module assignsexternally-generated data to the investigation. In some embodiments, thecomputer receives the externally-generated data from a POS system andthe externally-generated data includes at least one POS transaction. Insome embodiments, the particular user behavior may be defined by a modelof the particular user behavior. In embodiments, the video analyticsmodule includes a comparator module configured to compare the model of aparticular user behavior and the non-video data.

In some embodiments, the investigation generation module is configuredto simultaneously manage and populate a plurality of investigations.

In another aspect, the present disclosure is directed to a system togenerate real-time investigations of user behavior. An exampleembodiment of the system includes a video camera configured to capturevideo of user activity, a video analytic module to perform real-timevideo processing of the captured video to generates non-video data fromvideo, and a computer configured to receive the video and the non-videodata from the video camera. The computer includes a video analyticsmodule configured to analyze one of video and non-video data to identifyoccurrences of particular user behavior, and an investigation generationmodule configured to assign a video sequence related to the identifiedoccurrence of particular user behavior to an investigation. In someembodiments, investigation is generated in near real time. In someembodiments, the investigation generation module assignsexternally-generated data to the investigation. In some embodiments, thecomputer receives the externally-generated data from a POS system. Theexternally-generated data includes at least one POS transaction.

In some embodiments, the investigation generation module is configuredto simultaneously manage and populate a plurality of investigations. Insome embodiments, the particular user behavior is defined as at leastone of an action, an inaction, a movement, a plurality of eventoccurrences, a temporal event and an externally-generated event. In someembodiments, the particular user behavior is defined by a model of theparticular user behavior. The video analytics module includes acomparator module configured to compare the model of the particular userbehavior and the non-video data.

In yet another aspect, the present disclosure is directed to a method ofobserving behavior. In an example embodiment, the method includesreceiving video from a camera, and generating non-video data from thevideo. The non-video data includes non-video data related to userbehavior. The method includes identifying a particular user behavior,identifying at least one occurrence of the particular user behaviorwithin the video data, and generating an investigation related to theparticular user behavior.

In some embodiments, the method includes defining the particular userbehavior as at least one of an action, an inaction, a movement, aplurality of event occurrences, a temporal event, and anexternally-generated event.

In still another aspect, the present disclosure is directed tonon-transitory computer-readable medium comprising software formonitoring a point of sale (POS) transaction, which software, whenexecuted by a computer system, causes the computer system to receivevideo from a camera, generate non-video data from the video identifyinga particular user behavior, identify an occurrence of the particularuser behavior contained within the non-video data, and generate aninvestigation related to the identified occurrence of the particularuser behavior. In some embodiments, the investigation includes video ofthe occurrence of the particular user behavior contained within thenon-video data. In some embodiments, the software causes the computersystem to receive externally-generated POS transaction data from a POSsystems that includes at a least one individual transaction. In someembodiments, the software causes the computer to identify the particularuser behavior temporally related to the at least one individualtransaction. In some embodiments, the software causes the computer toprovide data related to the at least one individual transaction to theinvestigation. In some embodiments, the software causes the computer todefine the particular user behavior as at least one of an action, aninaction, a movement, a plurality of event occurrences, a temporal eventand an externally-generated event.

In a further aspect of the present disclosure, an example embodiment ofa non-transitory computer-readable medium includes software formonitoring user behavior, which software, when executed by a computersystem, causes the computer system to receive non-video data from acamera wherein the non-video data includes user behavioral informationdata, identify a particular user behavior, identify an occurrence of theparticular user behavior within the non-video data, identify video ofthe identified occurrence of the particular user behavior, and generatean investigation related to the identified occurrence of the particularuser behavior, the investigation including the identified video. In someembodiments, the software causes the computer to define the particularuser behavior as at least one of an action, an inaction, a movement, aplurality of event occurrences and a temporal event and anexternally-generated event. In some embodiments, the particular userbehavior is defined by a model of the particular user behavior. Theoccurrence identification step includes comparing the model of theparticular user behavior to the non-video data. In some embodiments, thesoftware causes the computer to receive externally-generated data from aPOS system, wherein the externally-generated data includes at least onePOS transaction, and identify an occurrence of the particular userbehavior within the non-video data related to the at least one POStransaction. In some embodiments, the software causes the computer toprovide data related to the at least one POS in the investigation.

A “video camera” may refer to an apparatus for visual recording.Examples of a video camera may include one or more of the following: avideo imager and lens apparatus; a video camera; a digital video camera;a color camera; a monochrome camera; a camera; a camcorder; a PC camera;a webcam; an infrared (IR) video camera; a low-light video camera; athermal video camera; a closed-circuit television (CCTV) camera; apan/tilt/zoom (PTZ) camera; and a video sensing device. A video cameramay be positioned to perform observation of an area of interest.

“Video” may refer to the motion pictures obtained from a video camerarepresented in analog and/or digital form. Examples of video mayinclude: television; a movie; an image sequence from a video camera orother observer; an image sequence from a live feed; a computer-generatedimage sequence; an image sequence from a computer graphics engine; animage sequence from a storage device, such as a computer-readablemedium, a digital video disk (DVD), or a high-definition disk (HDD); animage sequence from an IEEE 1394-based interface; an image sequence froma video digitizer; or an image sequence from a network.

“Video data” is a visual portion of the video.

“Non-video data” is non visual information extracted from the videodata.

A “video sequence” may refer to a selected portion of the video dataand/or the non-video data.

“Video processing” may refer to any manipulation and/or analysis ofvideo data, including, for example, compression, editing, and performingan algorithm that generates non-video data from the video.

A “frame” may refer to a particular image or other discrete unit withinvideo.

A “computer” may refer to one or more apparatus and/or one or moresystems that are capable of accepting a structured input, processing thestructured input according to prescribed rules, and producing results ofthe processing as output. Examples of a computer may include: acomputer; a stationary and/or portable computer; a computer having asingle processor, multiple processors, or multi-core processors, whichmay operate in parallel and/or not in parallel; a general purposecomputer; a supercomputer; a mainframe; a super mini-computer; amini-computer; a workstation; a micro-computer; a server; a client; aninteractive television; a web appliance; a telecommunications devicewith internet access; a hybrid combination of a computer and aninteractive television; a portable computer; a tablet personal computer(PC); a personal digital assistant (PDA); a portable telephone;application-specific hardware to emulate a computer and/or software,such as, for example, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application specific integratedcircuit (ASIC), an application specific instruction-set processor(ASIP), a chip, chips, or a chip set; a system on a chip (SoC), or amultiprocessor system-on-chip (MPSoC); an optical computer; a quantumcomputer; a biological computer; and an apparatus that may accept data,may process data in accordance with one or more stored softwareprograms, may generate results, and typically may include input, output,storage, arithmetic, logic, and control units.

“Software” may refer to prescribed rules to operate a computer. Examplesof software may include: software; code segments; instructions; applets;pre-compiled code; compiled code; interpreted code; computer programs;and programmed logic.

A “computer-readable medium” may refer to any storage device used forstoring data accessible by a computer. Examples of a computer-readablemedium may include: a magnetic hard disk; a floppy disk; an opticaldisk, such as a CD-ROM and a DVD; a magnetic tape; a flash removablememory; a memory chip; and/or other types of media that may storemachine-readable instructions thereon.

A “computer system” may refer to a system having one or more computers,where each computer may include a computer-readable medium embodyingsoftware to operate the computer. Examples of a computer system mayinclude: a distributed computer system for processing information viacomputer systems linked by a network; two or more computer systemsconnected together via a network for transmitting and/or receivinginformation between the computer systems; and one or more apparatusesand/or one or more systems that may accept data, may process data inaccordance with one or more stored software programs, may generateresults, and typically may include input, output, storage, arithmetic,logic, and control units.

A “network” may refer to a number of computers and associated devicesthat may be connected by communication facilities. A network may involvepermanent connections such as cables or temporary connections such asthose made through telephone or other communication links. A network mayfurther include hard-wired connections (e.g., coaxial cable, twistedpair, optical fiber, waveguides, etc.) and/or wireless connections(e.g., radio frequency waveforms, free-space optical waveforms, acousticwaveforms, etc.). Examples of a network may include: an internet, suchas the Internet; an intranet; a local area network (LAN); a wide areanetwork (WAN); and a combination of networks, such as an internet and anintranet. Exemplary networks may operate with any of a number ofprotocols, such as Internet protocol (IP), asynchronous transfer mode(ATM), and/or synchronous optical network (SONET), user datagramprotocol (UDP), IEEE 802.x, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a system block diagram of an embodiment of a videoobservation, surveillance and verification system in accordance with thepresent disclosure; and

FIG. 2 is a screen-shot of an embodiment of an investigation module 200displaying an investigation in accordance with the present disclosure.

DETAILED DESCRIPTION

Particular embodiments of the present disclosure are describedhereinbelow with reference to the accompanying drawings; however, it isto be understood that the disclosed embodiments are merely examples ofthe disclosure, which may be embodied in various forms. Well-knownfunctions or constructions are not described in detail to avoidobscuring the present disclosure in unnecessary detail. Therefore,specific structural and functional details disclosed herein are not tobe interpreted as limiting, but merely as a basis for the claims and asa representative basis for teaching one skilled in the art to variouslyemploy the present disclosure in virtually any appropriately detailedstructure. In this description, as well as in the drawings,like-referenced numbers represent elements which may perform the same,similar, or equivalent functions.

Additionally, the present disclosure may be described herein in terms offunctional block components, code listings, optional selections, pagedisplays, and various processing steps. It should be appreciated thatsuch functional blocks may be realized by any number of hardware and/orsoftware components configured to perform the specified functions. Forexample, embodiments of the present disclosure may employ variousintegrated circuit components, e.g., memory elements, processingelements, logic elements, look-up tables, and the like, which may carryout a variety of functions under the control of one or moremicroprocessors or other control devices.

Similarly, the software elements of the present disclosure may beimplemented with any programming or scripting language such as C, C++,C#, Java, COBOL, assembler, PERL, Python, PHP, or the like, with thevarious algorithms being implemented with any combination of datastructures, objects, processes, routines or other programming elements.The object code created may be executed on a variety of operatingsystems including, without limitation, Windows®, Macintosh OSX®, iOS®,linux, and/or Android®.

Further, it should be noted that embodiments of the present disclosuremay employ any number of conventional techniques for data transmission,signaling, data processing, network control, and the like. It should beappreciated that the particular implementations shown and describedherein are illustrative of the disclosure and its best mode and are notintended to otherwise limit the scope of the present disclosure in anyway. Examples are presented herein which may include sample data items(e.g., names, dates, etc.) which are intended as examples and are not tobe construed as limiting. Indeed, for the sake of brevity, conventionaldata networking, application development and other functional aspects ofthe systems (and components of the individual operating components ofthe systems) may not be described in detail herein. Furthermore, theconnecting lines shown in the various figures contained herein areintended to represent example functional relationships and/or physicalor virtual couplings between the various elements. It should be notedthat many alternative or additional functional relationships or physicalor virtual connections may be present in a practical electronic datacommunications system.

As will be appreciated by one of ordinary skill in the art, the presentdisclosure may be embodied as a method, a data processing system, adevice for data processing, and/or a computer program product.Accordingly, the present disclosure may take the form of an entirelysoftware embodiment, an entirely hardware embodiment, or an embodimentcombining aspects of both software and hardware. Furthermore,embodiments of the present disclosure may take the form of a computerprogram product on a computer-readable storage medium havingcomputer-readable program code means embodied in the storage medium. Anysuitable computer-readable storage medium may be utilized, includinghard disks, CD-ROM, DVD-ROM, optical storage devices, magnetic storagedevices, semiconductor storage devices (e.g., USB thumb drives) and/orthe like.

In the discussion contained herein, the terms “user interface element”and/or “button” are understood to be non-limiting, and include otheruser interface elements such as, without limitation, a hyperlink,clickable image, and the like.

Embodiments of the present disclosure are described below with referenceto block diagrams and flowchart illustrations of methods, apparatus(e.g., systems), and computer program products according to variousaspects of the disclosure. It will be understood that each functionalblock of the block diagrams and the flowchart illustrations, andcombinations of functional blocks in the block diagrams and flowchartillustrations, respectively, can be implemented by computer programinstructions. These computer program instructions may be loaded onto ageneral purpose computer, special purpose computer, mobile device orother programmable data processing apparatus to produce a machine, suchthat the instructions that execute on the computer or other programmabledata processing apparatus create means for implementing the functionsspecified in the flowchart block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meansthat implement the function specified in the flowchart block or blocks.The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer-implemented process such that theinstructions that execute on the computer or other programmableapparatus provide steps for implementing the functions specified in theflowchart block or blocks.

Accordingly, functional blocks of the block diagrams and flowchartillustrations support combinations of means for performing the specifiedfunctions, combinations of steps for performing the specified functions,and program instruction means for performing the specified functions. Itwill also be understood that each functional block of the block diagramsand flowchart illustrations, and combinations of functional blocks inthe block diagrams and flowchart illustrations, can be implemented byeither special purpose hardware-based computer systems that perform thespecified functions or steps, or suitable combinations of specialpurpose hardware and computer instructions.

One skilled in the art will also appreciate that, for security reasons,any databases, systems, or components of the present disclosure mayconsist of any combination of databases or components at a singlelocation or at multiple locations, wherein each database or systemincludes any of various suitable security features, such as firewalls,access codes, encryption, de-encryption, compression, decompression,and/or the like.

The scope of the disclosure should be determined by the appended claimsand their legal equivalents, rather than by the examples given herein.For example, the steps recited in any method claims may be executed inany order and are not limited to the order presented in the claims.Moreover, no element is essential to the practice of the disclosureunless specifically described herein as “critical” or “essential.”

With reference to FIG. 1 , a video observation, surveillance andverification system according to an embodiment of this disclosure isshown as 100. System 100 is a network video recorder that includes theability to record video from one or more cameras 110 (e.g. analog and/orIP camera). System 110 includes one or more video cameras 110 thatconnect to a computer 120 across a connection 130. Connection 130 may bean analog connection that provides video to the computer 120, a digitalconnection that provides a network connection between the video camera110 and the computer 120, or the connection 130 may include an analogconnection and a digital connection.

System 100 may include one or more video cameras 110 wherein each videocamera 110 connects to the computer 100 and a user interface 122 toprovide a user connection to the computer 120. The one or more videocameras 110 may each connect via individual connections, may connectthrough a common network connection, or through any combination thereof.

System 100 includes at least one video analytics module 120. A videoanalytics module 140 may reside in the computer 120 and/or one or moreof the video cameras 110. Video analytics module 140 performs videoprocessing of the video. In particular, video analytics module 140performs one or more algorithms to generate non-video data from video.Non-video data includes non-video frame data that describes content ofindividual frames such as, for example, objects identified in a frame,one or more properties of objects identified in a frame and one or moreproperties related to a pre-defined portions of a frame. Non-video datamay also include non-video temporal data that describes temporal contentbetween two or more frames. Non-video temporal data may be generatedfrom video and/or the non-video frame data. Non-video temporal dataincludes temporal data such as a temporal properties of an objectidentified in two or more frame and a temporal property of one or morepre-defined portions of two or more frames. Non-video frame data mayinclude a count of objects identified (e.g., objects may include peopleand/or any portion thereof, inanimate objects, animals, vehicles or auser defined and/or developed object) and one or more object properties(e.g., position of an object, position of any portion of an object,dimensional properties of an object, dimensional properties of portionsand/or identified features of an object) and relationship properties(e.g., a first object position with respect to a second object), or anyother object that may be identified in a frame. Objects may beidentified as objects that appear in video or objects that have beenremoved from video.

Video analytics module 140 positioned in a camera 110 converts video tovideo data and non-video data and the camera 110 and provides the videodata and the non-video data to the computer 120 over a network. As such,the system 100 distributes the video processing to the edge of thenetwork thereby minimizing the amount of processing required to beperformed by the computer 120.

Computer 120 may connect to an external system 150 that providesinformation related to the video. For example, external system 150 mayinclude a POS system that provides POS transaction information to thecomputer. Computer 120 may include a POS module 190 that receives POStransaction information and converts the POS transaction informationinto events. For example, the POS module 190 may receive datadescriptions of the content of the POS data. The POS module 190generates events based on user defined behavior discussed hereinbelow.

Computer 120 includes computer-readable medium comprising software formonitoring user behavior, which software, when executed by a computer120, causes the computer 120 to perform operations. User interface 122provides an interface to the computer 120. User interface 122 mayconnect directly to the computer 120 or connect indirectly to thecomputer 120 through a user network.

A user behavior is defined by an action, an inaction, a movement, aplurality of event occurrences, a temporal event, anexternally-generated event or any combination thereof. A particular userbehavior is defined and provided to the computer 120.

An action may include reaching for an object such as selecting a productfrom a retail shelf or retrieving an order ticket at a deli counter. Anaction may include picking up an object wherein the object has beenplaced or left at a particular location. An action may include moving aparticular object such as the opening of a door, drawer or compartment.An action may include positioning (or repositioning) a body part such asplacing a hand in a pocket after conducting retail transaction. Theaction may include moving to a particular position, a first individualengaging a second individual and/or moving a hand, arm, leg and/or footin a particular motion. An action may also include positioning a head ina particular direction, such as, for example, looking directly at amanager's office or security camera 110. Other examples are discussedhereinbelow. Actions may also include motions that result in unsanitaryconditions such as deli employees touching their faces during theprocess of filling deli orders.

Inaction may include failing to reach for an object wherein an object isdropped or positioned and the individual (e.g., object) does notretrieve the dropped object. Inaction may also include failing toperform a task that requires action, such as, for example, failing tooffer a sales receipt, change or cash back requested by a customerduring a debit transaction. Inaction may also include failing to placecash received from a customer into a register and/or cash slot. Inactionmay also include failing to make an observation that requires movementof the body or head, such as, for example, looking under a shopping cartduring a retail transaction. Inaction may also include failing to walkto a particular location or failure to perform a particular task. Forexample, confirming that a security door is locked would require theaction of approaching the door and the action of striking the door toensure that it would not open. As such, the user behavior may be definedas the inaction of approaching the door and/or the inaction of strikingthe door to confirm that the door will not open. In a health facility,an example of an inaction is failing to swipe a membership access card,indicative of a non-member entering the facility. An example of aninaction is a security guard failing to patrol assigned areas atspecified intervals or at specified times.

A movement may include movement generated by an electronic system, suchas, for example, advancing a portion of a customer receipt after aproduct is scanned or unwinding of a lottery ticket roll. Movement mayalso include the movement of items on a conveyor after a POStransaction.

A plurality of event occurrences may be a combination of relatedindividual events. For example, a plurality of events may include eventsthat require manager review or a plurality of events may include theremoval of an object from a security case and a subsequent POStransaction or a POS transaction that does not include an item locatedin the security case.

A temporal event may include the identification of a customer thatabruptly leaves a store, an individual dwelling at a store entrance orexit, an individual remaining in a particular location for an timeperiod exceeding a threshold.

Externally-generated events may include transactions provided from a POSsystem, an environmental control system (e.g., heating, cooling and/orlighting) or any other system capable of generating and/or providingexternal events. Externally-generated events may be provided via anysuitable connection, such as, for example, a hardwired connection and/ora network.

A user identifies a particular user behavior and provides and/or definescharacteristics of the particular user behavior in the computer 120.Computer 120 receives non-video data from the camera 110 wherein thenon-video data includes behavioral information data. The particular userbehavior may be defined by a model of the behavior where the modelincludes one or more attribute such a size, shape, length, width, aspectratio or any other suitable identifying or identifiable attribute. Thecomputer 120 includes a matching algorithm 195, such as a comparator,that compares the defined characteristics and/or model of the particularuser behavior with user behavior in the defined n the non-video data.Indication of a match by the matching algorithm 195 generates aninvestigation wherein the investigation includes the video data andnon-video data identified by the matching algorithm 195. Matchingalgorithm 195 may be configured as an independent module or incorporatedinto the video analytics module 140 in the computer 120 or in anycameras 110.

A particular user behavior may be defined as the placement of acashier's hand into a pocket within a preselected period after a POStransaction. This particular user behavior is indicative of a cashierfailing to return the correct change to a customer and “pocketing” thedifference. The video analytics module 140 performs an algorithm togenerate non-video data that identifies the cashier, identifies thecashier's hand and the movement of the cashier's hand. The POS module190 using data provided from the external system 150 (e.g., POS system)identifies an event that corresponds to the completion of a POStransaction and the matching algorithm 195 searches the non-video datawithin the predetermined period of time after the completion of the POStransaction to determine if the cashier's hand is placed in theirpocket. A temporal match in a POS transaction and hand placement in apocket results in the generation of an investigation.

Video analytics module 140 may include a comparator module configured tocompare the model of the particular user behavior and the non-videodata.

A particular user behavior may be defined as positioning a head towardan observation camera 110 exceeds a preset period or positioning of ahead directly toward a manager's office exceeds a preset period. Thisparticular user behavior is indicative of a customer trying to identifythe observation cameras 100 in a store in an effort to prevent beingdetected during a theft or an employee trying to determine if a manageris observing their behavior. The video analytics module 140 performs analgorithm to generate non-video data that identifies the head positionof objects. The video analytic module 140 may also provide a vectorindicating the facial direction. The matching algorithm 140 searches thenon-video data to determine if the head position and/or vectorindicating facial direction exceeds the preset period. A match resultsin the generation of an investigation.

A particular user behavior may be defined as a cashier failing toprovide a customer with cash back after a cash back debit card purchase(e.g., an inaction). A cash back debit transaction requires a cashier toperform two motions. The first motion is removing cash from the cashdrawer and the second motion is providing the cash to the customer.Failing to complete the first and second motions after a cash back debitcard transaction is indicative of a customer not receiving cash backfrom the transaction. The video analytics module 140 performs analgorithm to generate non-video data that identifies the cashier,identifies the cashier's hand and the movement of the cashier's hand.The POS module 190 identifies an event that corresponds to thecompletion of a cash back POS transaction and the matching algorithm 195searches the non-video data within the predetermined period after thecompletion of the POS transaction to determine if the cashier's handperformed the first and second motions. A match results in thegeneration of an investigation.

Investigations are a collection of data related to an identified event.The investigation simply documents behaviors of interest. As such,investigations require further review and investigation to understandthe particular behavior. Investigations may document customerpreferences such as why a customer selected a particular item, how acustomer shops for a particular item, and the amount of packaging detaila customer seeks before completing a selection. Other non-retailexamples include how customers select a table in a restaurant, theamount of time a customer spends reading a particular advertisement orwhich movie poster attracts customers' interests while walking to amovie.

In some instances, investigations uncover criminal activity. Forexample, an investigation generated after identifying the user behaviorof placing a cashier's hand into a pocket within a preselected periodafter a POS transaction includes a video sequence of the POStransaction. The investigation may also include a report of the POStransaction. A loss prevention individual is notified of the newlyopened investigation and the investigation can be reviewed through anysuitable user interface (e.g., computer, tablet PC, IPad, hand-heldsmart device or any other suitable device).

The loss prevention individual receives the investigation within secondsof the actual event. The video processing, POS transaction andprocessing, the matching algorithm 195 and generation of theinvestigation occur in near real time. As such, the investigation thatincludes all data and video required to view and assess the userbehavior is electronically transmitted to the loss preventionindividual's user interface.

An investigation generated after identifying the positioning of a headtoward an observation camera 110 for a preset period or positioning of ahead directly toward a manager's office for a preset period may includea plurality of video sequences related to the particular behavior. Forexample, an investigation is opened upon identification of the firstoccurrence of the user behavior. The investigation remains open andsubsequent occurrences of behavior (e.g., the same and/or other relatedbehavior) enters additional video sequences into the investigation. Theloss prevention individual is notified of the open investigation and mayobserve the further investigation while the system 100 continues topopulate the investigation with video sequences.

An investigation is populated with one or more video sequences of aparticular individual that demonstrates a user behavior or theinvestigation is populated with video sequences of any individual thatdemonstrates the user behavior. For example, shrinkage may be a resultof a single criminal or from a group of criminals working together. Assuch, the system 100 may be configured to populate the investigationwith video from any individuals that exhibit similar behavior, orinvestigations may be opened for a group of individuals that enter thestore together or make contact with each other in the store.

The system 100 provides the loss prevention individuals with tools,discussed hereinbelow, to amend and/or add to the contents of aninvestigation. For example, a loss prevention individual may add a videosequence or clip that clearly identifies the user's face, a videosequence that shows the individual entering the store and/or a videosequence that identifies additional criminal conduct. The lossprevention individual may also remove or amend a video sequenceautomatically entered into the investigation by the system 100.

An investigation may be connected to a particular employee. Employee maybe indentified by an identification number, such as, for example, anidentification number entered into the POS system. The investigation mayremain open thereby forming an ongoing investigation that is populatedwith additional video sequences as behaviors of the particular employeeare observed. For example, an employee may be the target of an ongoinginternal investigation. As such, video sequences identified by thesystem are entered into the ongoing internal investigation related tothis particular employee wherein POS data is used to identify theemployee.

FIG. 2 is a screen-shot of the investigation module 200 displaying aninvestigation generated in accordance of an embodiment of thisdisclosure. Investigation module 200 is configured to generate and storeinformation required to document a particular user behavior.

Investigation module 200 includes a viewing window 210 with upper andlower viewing control bars 212 a, 212 b, a text entry window 214, atimeline window 220, a camera window 230, a search window 240, aplayback option window 250, a clip option window 260 and a filemaintenance window 270.

Investigations automatically generated by the system 100 are populatedwith information related to the particular user behavior as discussedhereinabove. For example, the investigation illustrated in FIG. 2includes a first video sequence 220 a and a second video sequence 220 bwherein the first video sequence 220 a is from the downstairs camera andthe second video sequence 220 b is from a camera located at theelevator. In one embodiment, the first video sequence 220 a was providedthrough an automatically generated investigation and the automaticallygenerated investigation was provided to the loss prevention individual.

The first video sequence 220 a is selected in the timeline window 220and played in the viewing window 210. To further this explanation andfor example, suppose the loss prevention individual, upon viewing thefirst video sequence 220 a on a PDA, observes an individual removing acompany laptop computer from the downstairs area. In generating theinvestigation, the system identified this user behavior as a particularuser behavior and upon review, the loss prevention individual concursthat the automatically generated investigation has merit and escalatedthe automatically generated investigation to a theft investigation.

Keep in mind, the automatically generated investigation was provided tothe loss prevention individual in near real-time, therefore, theindividual now in possession of the company laptop may have only taken afew steps from where the laptop was removed.

Using the PDA, the loss prevention individual furthers the automaticallygenerated investigation (now a theft investigation) by observingtemporally related video and video data available through theinvestigation module 200 on a PDA.

The search window 240 may automatically select a timeframe related tothe investigation. The timeline may be manually controlled through thePDA.

Video and/or video data from one or more cameras listed in the camerawindow 230 may be selected for viewing in the viewing window 210. Aplurality of video streams from individual cameras (see FIG. 1 ) may beviewed simultaneously by selecting an alternative viewing screen fromthe upper viewing control bar 212 a.

The lower viewing control bar 212 b allows viewing video in the viewingwindow 210 in real time or other selected speeds. The investigationmodule 200 provides an investigation playback speed wherein the playbackspeed is automatically calculated to replay video at a playback speedthat requires the loss prevention individual to view every frame of thevideo sequence. Video is recorded and saved at speeds that exceed theability of a human eye to detect slight movements. Additionally, theplayback device may also have hardware and/or software limitations thatprevent the playback device from displaying every frame of video. Assuch, playback of video at “real time” results in missing individualframes of video due to human viewing limitations and/or computer displaylimitations. The investigation playback speed is calculated based on thehuman viewing limitations and the display limitations of the particulardevice being used to view the investigation module 200.

Playback option window 250 allows the video sequence and/or the videofrom each camera to be played in various modes. The all frame displaymode plays video at the calculated investigation playback speed whereinall frames are displayed and viewable during playback. The motion onlydisplay mode provides video sequences of the video that include motion.The trigger only display mode includes video sequences temporallyrelated to a trigger.

Triggers include internal triggers and/or external triggers. Internaltriggers include motion triggers defined by a user and determined by thevideo analytics module 140, POS triggers generated by the POS module 190and analytics events defined by a tripline and/or a zone (e.g., enteringand/or exiting a zone) and determined by the video analytics module 140.External triggers are generated by external hardware devices connecteddirectly or indirectly to the computer 110.

At any point of the investigation the loss prevention individual mayassign a video sequence to the timeline. For example, in FIG. 2 the lossprevention individual has added the second video sequence 220 b to theinvestigation. The second video sequence 220 b includes video providedfrom a camera positioned at the elevator and stairway. To further thescenario described hereinabove, suppose the loss prevention individualidentified a suspect carrying the laptop and approaching an elevatordisplayed in the second video sequence 220 b. In furtherance of thetheft investigation, the loss prevention individual included the secondvideo sequence 220 b in the timeline of the investigation.

Loss prevention individual may select various options from the videoclip window 260. The timeline window 220 may be populated with videoclips including one or more video sequences, a still image generatedfrom the video or text entered through the text entry window 214. Avideo clip may include a continuous video sequence. Alternatively, avideo clip using the playback option of motion only (selected in theplayback option window 250) includes a plurality of video sequences thatinclude motion (e.g., non-motion portions of the video are excluded fromthe video clip). Finally, the loss prevention individual may capture astill image of a frame to capture an individual feature such as a facialimage, a particular tool or object used during the theft, or any othersignificant image that may be required to further the investigation.

Finally, since the investigation is generated in near real-time, theloss prevention individual, upon confirmation of a theft currently inprogress, is able to notify security and apprehend the thief before theyare able to leave the premises.

As various changes could be made in the above constructions withoutdeparting from the scope of the disclosure, it is intended that allmatter contained in the above description shall be interpreted asillustrative and not in a limiting sense. It will be seen that severalobjects of the disclosure are achieved and other advantageous resultsattained, as defined by the scope of the following claims.

What is claimed is:
 1. A video analytics module, comprising: a memorystoring user behavior type data indicating a plurality of types of userbehaviors; and a processor configured to: receive video capture devicedata from a video capture device; identify user behavior from the videocapture device data; receive point-of-sale data; identify at least onepoint-of-sale transaction from the point-of-sale data that correspondsto the user behavior; match the user behavior to at least one of theplurality of types of user behaviors, the user behavior that occurwithin a predetermined time window relative to completion of the atleast one point-of-sale transaction; and generate investigation databased on identification of at least one point-of-sale transactioncorresponding to the matched user behavior.
 2. The video analyticsmodule according to claim 1, wherein the memory further stores rule dataincluding a plurality of rules associated with the plurality of types ofuser behaviors.
 3. The video analytics module according to claim 1,wherein the plurality of types of user behaviors includes at least oneof an action, an inaction, a movement, a plurality of event occurrences,a temporal event, or an externally-generated event.
 4. The videoanalytics module according to claim 1, wherein the investigation data isgenerated concurrently with receipt of the video capture device data andthe point-of-sale transaction.
 5. The video analytics module accordingto claim 1, wherein the investigation data contains a video sequenceincluding the user behavior.
 6. The video analytics module according toclaim 1, wherein the processor is further configured to transmit theinvestigation data over a network to a computing device.
 7. The videoanalytics module according to claim 1, wherein the plurality of types ofuser behaviors includes an individual concealing at least one hand. 8.The video analytics module according to claim 1, wherein the pluralityof types of user behaviors includes an individual positioning a headtoward the video capture device for a predetermined time.
 9. The videoanalytics module according to claim 1, wherein the video capture devicedata includes video data and non-video data.
 10. A system for generatinginvestigation data based on user behavior, the system comprising: avideo capture device configured to capture video capture device data; apoint-of-sale (POS) system configured to generate point-of-saletransaction information pertaining to at least one point-of-saletransaction; a computing device coupled to the video capture device andthe POS system, the computing device including: a memory storing userbehavior type data indicating a plurality of types of user behaviors;and a processor configured to: receive video capture device data from avideo capture device; identify user behavior from the video capturedevice data; receive point-of-sale data; identify at least onepoint-of-sale transaction from the point-of-sale data that correspondsto the user behavior; match the user behavior to at least one of theplurality of types of user behaviors, the user behavior that occurwithin a predetermined time window relative to completion of the atleast one point-of-sale transaction; and generate investigation databased on identification of at least one point-of-sale transactioncorresponding to the matched user behavior.
 11. The system according toclaim 10, wherein the memory further stores rule data including aplurality of rules associated with the plurality of types of userbehaviors.
 12. The system according to claim 10, wherein the pluralityof types of user behaviors includes at least one of an action, aninaction, a movement, a plurality of event occurrences, a temporalevent, or an externally-generated event.
 13. The system according toclaim 10, wherein the investigation data is generated concurrently withreceipt of the video capture device data and the point-of-saletransaction information.
 14. The system according to claim 10, whereinthe investigation data contains a video sequence including the userbehavior.
 15. The system according to claim 10, wherein the processor isfurther configured to transmit the investigation data over a network toa computing device.
 16. The system according to claim 10, wherein theplurality of types of user behaviors includes an individual concealingat least one hand.
 17. The system according to claim 10, wherein theplurality of types of user behaviors includes an individual positioninga head toward the video capture device for a predetermined time.
 18. Thesystem according to claim 10, wherein the video capture device dataincludes video data and non-video data.